مقایسهٔ روش‌ها

	واحد تکرارشونده گیت‌دار (GRU)	شبکه عصبی بازگشتی
حوزه	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning
سال پیدایش	2014	1986–1990
پدیدآور	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.	Rumelhart, D. E.; Elman, J. L.
نوع	Recurrent neural network with gating	Sequential neural network
منبع بنیادین	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014, pp. 1724–1734.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
نام‌های دیگر	GRU, GRU network, gated RNN, GRU cell	RNN, Elman network, Jordan network, simple recurrent network
مرتبط	3	3
خلاصه	The Gated Recurrent Unit (GRU), introduced by Cho et al. in 2014, is a streamlined recurrent neural network that uses two learned gates — an update gate and a reset gate — to selectively retain or discard information across time steps, enabling effective sequence modelling with fewer parameters than LSTM.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.